Abstract:Background and objective: Early identification of individuals at high risk of chronic obstructive pulmonary disease (COPD) is crucial for reducing related mortality rates and economic burden. However, conventional machine learning (ML) models have limitations when making predictions using COPD data that exhibit high-dimensional and unbalanced characteristics. Therefore, to address this issue, this study developed a well-performing Bayesian optimization (BO)-ML hybrid model combined with variable screening and … Show more
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